The Inference Bottleneck: Antitrust and Neutrality Duties in the Age of Cognitive Infrastructure
Gaston Besanson, Marcelo Celani

TL;DR
This paper explores how large-scale AI inference acts as a cognitive infrastructure that can be exploited for anticompetitive practices beyond pricing, proposing a new framework for regulation based on transparency and non-discrimination.
Contribution
It introduces the concept of cognitive infrastructure in AI, develops a framework for analyzing non-price foreclosure, and proposes Neutral Inference as a regulatory approach.
Findings
Inference bottleneck is a critical competitive asset.
Foreclosure can occur through non-price means like latency and routing.
Neutral Inference offers a targeted, auditable regulation approach.
Abstract
As generative AI commercializes, competitive advantage is shifting from one-time model training toward continuous inference, distribution, and routing. At the frontier, large-scale inference can function as cognitive infrastructure: a bottleneck input that downstream applications rely on to compete, controlled by firms that often compete downstream through integrated assistants, productivity suites, and developer tooling. Foreclosure risk is not limited to price. It can be executed through non-price discrimination (latency, throughput, error rates, context limits, feature gating) and, where models select tools and services, through steering and default routing that is difficult to observe and harder to litigate. This essay makes three moves. First, it defines cognitive infrastructure as a falsifiable concept built around measurable reliance, vertical incentives, and discrimination…
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Taxonomy
TopicsDigital Platforms and Economics · Ethics and Social Impacts of AI · Blockchain Technology Applications and Security
